
import pandas as pd
import numpy as np
from itertools import product
from collections import defaultdict

# Stationarity tests
from statsmodels.tsa.stattools import adfuller, kpss

# Autocorr test
from statsmodels.stats.diagnostic import acorr_ljungbox

# Autocorrelation analysis
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

#Metric evaluation
from utilsforecast.losses import rmse, mape, mae
from utilsforecast.evaluation import evaluate

# Visualizations
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
from scipy.stats import norm
import scipy.stats as stats


#Ridge regression
!pip install mlforecast==1.0.1

from mlforecast import MLForecast
from mlforecast.lag_transforms import ExpandingMean, RollingMean
from mlforecast.target_transforms import Differences

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import Ridge
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

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#XGboost
!pip install xgboost==2.1.3

import xgboost as xgb

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#ARIMA

!pip install statsforecast==2.0.0

from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
from statsforecast.arima import arima_string

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#LSTM
!pip install neuralforecast==2.0.1 

import logging

from neuralforecast import NeuralForecast
from neuralforecast.models import LSTM, NHITS, RNN

logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)

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#Prophet
!pip install prophet==1.1.6

import prophet as fp
from prophet.diagnostics import performance_metrics, cross_validation

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import warnings
warnings.filterwarnings('ignore')


#Create new file with logs every run for model versioning and model metrics logging

import logging, datetime, sys

dateTag = datetime.datetime.now().strftime("%d-%b-%Y_%H-%M-%S")
model_metrics_logger = logging.getLogger('model_metrics')

model_metrics_logger.handlers.clear()
model_metrics_logger.addHandler(logging.FileHandler("model_metrics_%s.log" % dateTag))
model_metrics_logger.addHandler(logging.StreamHandler(sys.stdout))
model_metrics_logger.setLevel(level=logging.INFO)


random_state = 42


def evaluate_cross_validation(df, metrics): 
    """
    Calculate metric score for cross-validation
    """
    models = df.drop(columns=['unique_id', 'ds', 'cutoff', 'y']).columns.tolist()
    evals = []
    # Calculate loss for every unique_id and cutoff.
    for cutoff in df['cutoff'].unique():
        eval_ = evaluate(df[df['cutoff'] == cutoff], metrics=metrics, models=models)
        
        evals.append(eval_)
    evals = pd.concat(evals)
    evals = evals.groupby(['unique_id', 'metric']).mean(numeric_only=True) # Averages the error metrics for all cutoffs for every combination of model and unique_id
    return evals


class GridSearch:
    """
    Class for making grid search of hyperparameters for models by using cross-validation.
    """
    def __init__(self, all_params, model_initializer, calculate_score):
        self.all_params = all_params
        self.calculate_score = calculate_score
        self.initialize_model = model_initializer
        self.results_df = None
        self.best_params = None
        self.best_score = None

    
    def search(self):
        """ Evaluate vie cross-validation score for each set of hyperparameters from grid and write them to result DataFrame.
        """
        params_product = self.__params_product()        
        results_dict = dict()
               
        for item in enumerate(params_product):
            
            index = item[0]
            params = item[1]
            
            model = self.initialize_model(params)
            
            current_score = self.calculate_score(model)               
            
            if self.best_score is None or current_score < self.best_score:
                self.best_score = current_score
                self.best_params = params
            
            
            print(f'Iteration: {index + 1} of {len(params_product)}\n\tParams: {params}\n\tScore: {current_score}')
            results_dict.setdefault('score', []).append(current_score)
            self.__add_to_results(results_dict, params)
            
        
        print(f'Best score: {self.best_score} \n\twith params {self.best_params}')
        
        self.results_df = pd.DataFrame(results_dict).sort_values(by='score')
        
        
        
    def display_results(self):   
        """ Display result table with hyperparameters sorted by score.
        """
        display(self.results_df)
        
        
    def get_best_params(self):
        """ Return hyperparameters which showed higher score on cross-validation.
        """
        return self.best_params
        
        
    def __add_to_results(self, results, params):
        for k, v in params.items():
                result_params = results.setdefault(k, [])
                result_params.append(v)
        
        
            
    
    def __params_product(self):
        return [dict(zip(self.all_params.keys(), vcomb)) for vcomb in product(*self.all_params.values())]


def my_mape(predictions, targets):
    """ Calculate MAPE score.
    """
    return np.mean(np.abs((predictions - targets) / predictions)) * 100


def adf_test(timeseries):
    """ Calculate ADF test statistic.
    """
    print("Results of Dickey-Fuller Test:")
    dftest = adfuller(timeseries, autolag="AIC")
    dfoutput = pd.Series(
        dftest[0:4],
        index=[
            "Test Statistic",
            "p-value",
            "#Lags Used",
            "Number of Observations Used",
        ],
    )
    for key, value in dftest[4].items():
        dfoutput["Critical Value (%s)" % key] = value
    print(dfoutput)


def plot_residuals(residuals):
    """ Plot for residuals analyzing:
        1. Residuals Plot. 
        2. Histogram plus estimated density.
        3. Normal Q-Q Plot.
        4. Correlogram.
    """
    fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))

    # plot[1,1]
    residuals.plot(ax=axs[0,0])
    axs[0,0].set_title("Residuals")

    # plot
    sns.distplot(residuals, ax=axs[0,1], fit=norm) # add fit=norm to visualize norm distribution
    axs[0,1].set_title("Density plot - Residual")

    # plot
    stats.probplot(residuals, dist="norm", plot=axs[1,0])
    axs[1,0].set_title('Plot Q-Q')

    # plot
    plot_acf(residuals,  lags=35, ax=axs[1,1],color="fuchsia")
    axs[1,1].set_title("Autocorrelation")

    plt.show()


nvidia_stocks_full = pd.read_csv('NVDA_230122_250122.csv')
nvidia_stocks_full.head()


nvidia_stocks_full.index=pd.to_datetime(nvidia_stocks_full['<DATE>']+nvidia_stocks_full['<TIME>'],format="%d/%m/%y%H:%M:%S")
nvidia_stocks_full.index.names = ['ds']

nvidia_stocks_full.head()


nvidia_stocks = nvidia_stocks_full.copy()

nvidia_stocks.rename(columns={'<CLOSE>':'y'}, inplace=True)
nvidia_stocks = nvidia_stocks[['y']]

nvidia_stocks = nvidia_stocks.resample('D').mean().interpolate()
nvidia_stocks['unique_id'] = 'stock_id'

nvidia_stocks.reset_index(inplace=True)

nvidia_stocks


fig = px.line(nvidia_stocks, x=nvidia_stocks['ds'], y=nvidia_stocks['y'],
              title="Nvidia closing prices (1 day)")

fig.update_layout(template='plotly_white', width=800, height=500)
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="closing price")
fig.show()


train_size = int(len(nvidia_stocks) * 0.8)
train, test = nvidia_stocks[:train_size], nvidia_stocks[train_size:]


fig = px.line(title="Nvidia closing prices (1 day)")
fig.add_scatter(x=train['ds'], y=train['y'], mode='lines', name='train', line=dict(color='blue'))
fig.add_scatter(x=test['ds'], y=test['y'], mode='lines', name='test', line=dict(color='green'))

fig.update_layout(template='plotly_white', width=1000, height=500)
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="closing price")
fig.show()


adf_test(train['y'])

Results of Dickey-Fuller Test:
Test Statistic                   0.026515
p-value                          0.960671
#Lags Used                      19.000000
Number of Observations Used    564.000000
Critical Value (1%)             -3.441998
Critical Value (5%)             -2.866678
Critical Value (10%)            -2.569506
dtype: float64


train_diff = train.copy()
train_diff['diff'] = train_diff['y'].diff()
train_diff.head()


fig = px.line(train_diff, x=train_diff.index, y=train_diff['diff'],
              title="Nvidia closing prices (daily) - diff(1))")

fig.update_layout(template='plotly_white', width=800, height=500)
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="closing price")
fig.show()


adf_test(train_diff['diff'].dropna())

Results of Dickey-Fuller Test:
Test Statistic                  -5.169163
p-value                          0.000010
#Lags Used                      18.000000
Number of Observations Used    564.000000
Critical Value (1%)             -3.441998
Critical Value (5%)             -2.866678
Critical Value (10%)            -2.569506
dtype: float64


# 2 graphs side by side
fig, ax = plt.subplots(1, 2, figsize=(10, 4))

# ACF
plot_acf(train['y'], ax=ax[0], lags=20)
ax[0].set_title('ACF')

# PACF
plot_pacf(train['y'], ax=ax[1], lags=20)
ax[1].set_title('PACF')


plt.tight_layout()
plt.show()


def linear_regression_model_init(params):
    
    if params['standard_scaller']:
        linear_regression_model = Pipeline(steps=[
            ('scaller', StandardScaler()),
            ('poly', PolynomialFeatures(params['polynomial'])),
            ('estimator', Ridge(alpha=params['ridge_alpha'], random_state=random_state)),
        ])
    else:
        linear_regression_model = Pipeline(steps=[
            ('poly', PolynomialFeatures(params['polynomial'])),
            ('estimator', Ridge(alpha=params['ridge_alpha'], random_state=random_state)),
        ])
    
    
    return MLForecast(
        freq='D',
        models=[
            linear_regression_model,
        ],
        lags=params['lags'],
        target_transforms=[Differences([1])]
    )


def linear_regression_cv_score(model): 
    cv_results = model.cross_validation(
        df = train,
        h = 200,
        step_size = 40,
        n_windows = 5
    )
    
    cv_results = evaluate_cross_validation(cv_results, [mape])
    
    return cv_results.loc['stock_id', 'mape']['Ridge']


grid_search_linear_regression = GridSearch(
    all_params={
        'lags':[[1], [1, 2], [1, 2, 3]],
        'polynomial': [1, 2],
        'standard_scaller': [True, False],
        'ridge_alpha': [0.01, 0.1, 1, 10, 100]
    },
    model_initializer=linear_regression_model_init,
    calculate_score=linear_regression_cv_score
)


grid_search_linear_regression.search()

Iteration: 1 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 0.01}
	Score: 0.20911131132312272
Iteration: 2 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 0.1}
	Score: 0.2091111316402034
Iteration: 3 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 1}
	Score: 0.20910934129980524
Iteration: 4 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 10}
	Score: 0.2090920638325259
Iteration: 5 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 100}
	Score: 0.20896510127159257
Iteration: 6 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 0.01}
	Score: 0.20911130140288706
Iteration: 7 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 0.1}
	Score: 0.20911103251946456
Iteration: 8 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 1}
	Score: 0.2091083581977255
Iteration: 9 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 10}
	Score: 0.2090829906431936
Iteration: 10 of 60
	Params: {'lags': [1], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 100}
	Score: 0.20891814999147273
Iteration: 11 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 0.01}
	Score: 0.20850349319010034
Iteration: 12 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 0.1}
	Score: 0.20850546634269884
Iteration: 13 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 1}
	Score: 0.20852511397665877
Iteration: 14 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 10}
	Score: 0.20871353039410517
Iteration: 15 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 100}
	Score: 0.21003153921468165
Iteration: 16 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 0.01}
	Score: 0.2085036504266946
Iteration: 17 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 0.1}
	Score: 0.20850703719052
Iteration: 18 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 1}
	Score: 0.20854067185601582
Iteration: 19 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 10}
	Score: 0.20885517968708758
Iteration: 20 of 60
	Params: {'lags': [1], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 100}
	Score: 0.21070094727251631
Iteration: 21 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 0.01}
	Score: 0.20885274576747187
Iteration: 22 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 0.1}
	Score: 0.20885263964653822
Iteration: 23 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 1}
	Score: 0.20885158208758736
Iteration: 24 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 10}
	Score: 0.20884135948285493
Iteration: 25 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 100}
	Score: 0.20876543646151763
Iteration: 26 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 0.01}
	Score: 0.20885274019468714
Iteration: 27 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 0.1}
	Score: 0.2088525839611885
Iteration: 28 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 1}
	Score: 0.20885102945646178
Iteration: 29 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 10}
	Score: 0.20883623057684236
Iteration: 30 of 60
	Params: {'lags': [1, 2], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 100}
	Score: 0.20873808875042058
Iteration: 31 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 0.01}
	Score: 0.20853693235162013
Iteration: 32 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 0.1}
	Score: 0.20853869991928958
Iteration: 33 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 1}
	Score: 0.2085563064005161
Iteration: 34 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 10}
	Score: 0.20872571324944808
Iteration: 35 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 100}
	Score: 0.20994113261096176
Iteration: 36 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 0.01}
	Score: 0.20853708904577503
Iteration: 37 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 0.1}
	Score: 0.208540265448418
Iteration: 38 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 1}
	Score: 0.2085718215485835
Iteration: 39 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 10}
	Score: 0.20886787073446672
Iteration: 40 of 60
	Params: {'lags': [1, 2], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 100}
	Score: 0.21064548173904435
Iteration: 41 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 0.01}
	Score: 0.20946533961726282
Iteration: 42 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 0.1}
	Score: 0.2094652052413291
Iteration: 43 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 1}
	Score: 0.20946386670849573
Iteration: 44 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 10}
	Score: 0.20945098481425148
Iteration: 45 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': True, 'ridge_alpha': 100}
	Score: 0.20935853526532228
Iteration: 46 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 0.01}
	Score: 0.20946533340876888
Iteration: 47 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 0.1}
	Score: 0.2094651432101101
Iteration: 48 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 1}
	Score: 0.20946325173055264
Iteration: 49 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 10}
	Score: 0.2094453333087695
Iteration: 50 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 1, 'standard_scaller': False, 'ridge_alpha': 100}
	Score: 0.20933047158511334
Iteration: 51 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 0.01}
	Score: 0.2124074558917921
Iteration: 52 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 0.1}
	Score: 0.21240886287050334
Iteration: 53 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 1}
	Score: 0.21242285488922202
Iteration: 54 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 10}
	Score: 0.21255550845905918
Iteration: 55 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 100}
	Score: 0.21343769199943607
Iteration: 56 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 0.01}
	Score: 0.21240764808854426
Iteration: 57 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 0.1}
	Score: 0.21241078181062817
Iteration: 58 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 1}
	Score: 0.21244174635021054
Iteration: 59 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 10}
	Score: 0.212718723957374
Iteration: 60 of 60
	Params: {'lags': [1, 2, 3], 'polynomial': 2, 'standard_scaller': False, 'ridge_alpha': 100}
	Score: 0.2140486006738273
Best score: 0.20850349319010034 
	with params {'lags': [1], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 0.01}


grid_search_linear_regression.display_results()


linear_regression_model = linear_regression_model_init(grid_search_linear_regression.get_best_params())


linear_regression_model.fit(train, fitted=True)

MLForecast(models=[Ridge], freq=D, lag_features=['lag1'], date_features=[], num_threads=1)


fitted_predictions = linear_regression_model.forecast_fitted_values()
fitted_predictions['residuals'] = fitted_predictions['y'] - fitted_predictions['Ridge']
plot_residuals(fitted_predictions['residuals'])


predictions = linear_regression_model.predict(len(test))

predictions.set_index(test.index, inplace=True)


fig = px.line(title="Nvidia closing prices (daily)")
fig.add_scatter(x=train['ds'], y=train['y'], mode='lines', name='train', line=dict(color='blue'))
fig.add_scatter(x=test['ds'], y=test['y'], mode='lines', name='test', line=dict(color='green'))
fig.add_scatter(x=predictions['ds'], y=predictions['Ridge'], mode='lines', name='prediction', line=dict(color='red'))

fig.update_layout(template='plotly_white', width=1000, height=500)
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="closing price")
fig.show()


lineral_regression_mape = my_mape(predictions['Ridge'], test['y'])


model_metrics_logger.info(
    f'Model name: Ridge, MAPE score: {lineral_regression_mape}, random_state: {random_state}, hyperparameters: {grid_search_linear_regression.get_best_params()}'
)

Model name: Ridge, MAPE score: 6.104782628986853, random_state: 42, hyperparameters: {'lags': [1], 'polynomial': 2, 'standard_scaller': True, 'ridge_alpha': 0.01}


def xgboost_model_init(params):    
    return MLForecast(
        freq='D',
        models=[
            xgb.XGBRegressor(
                objective='reg:squarederror',
                random_state=random_state,
                max_depth=params['max_depth'],
                reg_lambda=params['reg_lambda'],
                eta=params['eta']
            )
        ],
        lags=params['lags'],
        target_transforms=[Differences([1])]
    )


def xgboost_cv_score(model): 
    cv_results = model.cross_validation(
        df = train,
        h = 200,
        step_size = 40,
        n_windows = 5
    )
    
    cv_results = evaluate_cross_validation(cv_results, [mape])
    
    return cv_results.loc['stock_id', 'mape']['XGBRegressor']


xgboost_grid_search = GridSearch(
    all_params={
        'lags':[[1, 2, 3, 4, 5]],
        'max_depth': [2, 4, 6],
        'reg_lambda': [0.01, 0.1, 1, 10, 100],
        'eta': [0.2, 0.3, 0.4]
    },
    model_initializer=xgboost_model_init,
    calculate_score=xgboost_cv_score
)


xgboost_grid_search.search()

Iteration: 1 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 0.01, 'eta': 0.2}
	Score: 0.21496090241549845
Iteration: 2 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 0.01, 'eta': 0.3}
	Score: 0.2091375652148184
Iteration: 3 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 0.01, 'eta': 0.4}
	Score: 0.1770481708395748
Iteration: 4 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 0.1, 'eta': 0.2}
	Score: 0.21807720901071428
Iteration: 5 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 0.1, 'eta': 0.3}
	Score: 0.22219061187735906
Iteration: 6 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 0.1, 'eta': 0.4}
	Score: 0.19924969465729603
Iteration: 7 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 1, 'eta': 0.2}
	Score: 0.21317747838860407
Iteration: 8 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 1, 'eta': 0.3}
	Score: 0.23131812310922614
Iteration: 9 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 1, 'eta': 0.4}
	Score: 0.20222303289053212
Iteration: 10 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 10, 'eta': 0.2}
	Score: 0.1967312563403099
Iteration: 11 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 10, 'eta': 0.3}
	Score: 0.19177274043863582
Iteration: 12 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 10, 'eta': 0.4}
	Score: 0.20283706121393932
Iteration: 13 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 100, 'eta': 0.2}
	Score: 0.1974831426099958
Iteration: 14 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 100, 'eta': 0.3}
	Score: 0.19029211659589182
Iteration: 15 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 2, 'reg_lambda': 100, 'eta': 0.4}
	Score: 0.1900599558983575
Iteration: 16 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 0.01, 'eta': 0.2}
	Score: 0.23667772467268477
Iteration: 17 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 0.01, 'eta': 0.3}
	Score: 0.21597653815092918
Iteration: 18 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 0.01, 'eta': 0.4}
	Score: 0.23940994745685326
Iteration: 19 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 0.1, 'eta': 0.2}
	Score: 0.20850908536838525
Iteration: 20 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 0.1, 'eta': 0.3}
	Score: 0.2169898345832431
Iteration: 21 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 0.1, 'eta': 0.4}
	Score: 0.2144278962259493
Iteration: 22 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 1, 'eta': 0.2}
	Score: 0.2235975014582839
Iteration: 23 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 1, 'eta': 0.3}
	Score: 0.2079727173266454
Iteration: 24 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 1, 'eta': 0.4}
	Score: 0.20634182566389686
Iteration: 25 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 10, 'eta': 0.2}
	Score: 0.17300700467801663
Iteration: 26 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 10, 'eta': 0.3}
	Score: 0.17878277774715495
Iteration: 27 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 10, 'eta': 0.4}
	Score: 0.19376698152812116
Iteration: 28 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 100, 'eta': 0.2}
	Score: 0.19278458049844813
Iteration: 29 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 100, 'eta': 0.3}
	Score: 0.1876839170343645
Iteration: 30 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 4, 'reg_lambda': 100, 'eta': 0.4}
	Score: 0.20122164675218449
Iteration: 31 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 0.01, 'eta': 0.2}
	Score: 0.1924537335858931
Iteration: 32 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 0.01, 'eta': 0.3}
	Score: 0.19504338245705935
Iteration: 33 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 0.01, 'eta': 0.4}
	Score: 0.1693674819642384
Iteration: 34 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 0.1, 'eta': 0.2}
	Score: 0.21870366193769036
Iteration: 35 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 0.1, 'eta': 0.3}
	Score: 0.1987810877530689
Iteration: 36 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 0.1, 'eta': 0.4}
	Score: 0.19202114630864497
Iteration: 37 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 1, 'eta': 0.2}
	Score: 0.2051947840320994
Iteration: 38 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 1, 'eta': 0.3}
	Score: 0.18525838579034684
Iteration: 39 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 1, 'eta': 0.4}
	Score: 0.1993262815355879
Iteration: 40 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 10, 'eta': 0.2}
	Score: 0.19964462079849568
Iteration: 41 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 10, 'eta': 0.3}
	Score: 0.1896809931303151
Iteration: 42 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 10, 'eta': 0.4}
	Score: 0.18649161594524422
Iteration: 43 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 100, 'eta': 0.2}
	Score: 0.2014185615681492
Iteration: 44 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 100, 'eta': 0.3}
	Score: 0.18859486100557804
Iteration: 45 of 45
	Params: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 100, 'eta': 0.4}
	Score: 0.18934122521420732
Best score: 0.1693674819642384 
	with params {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 0.01, 'eta': 0.4}


xgboost_grid_search.display_results()


xgboost_model = xgboost_model_init(xgboost_grid_search.get_best_params())
xgboost_model.fit(train, fitted=True)

MLForecast(models=[XGBRegressor], freq=D, lag_features=['lag1', 'lag2', 'lag3', 'lag4', 'lag5'], date_features=[], num_threads=1)


fitted_xgboost_predictions = xgboost_model.forecast_fitted_values()
fitted_xgboost_predictions['residuals'] = fitted_xgboost_predictions['y'] - fitted_xgboost_predictions['XGBRegressor']
plot_residuals(fitted_xgboost_predictions['residuals'])


xgboost_predictions = xgboost_model.predict(len(test))

xgboost_predictions.set_index(test.index, inplace=True)


fig = px.line(title="Nvidia closing prices (daily)")
fig.add_scatter(x=train['ds'], y=train['y'], mode='lines', name='train', line=dict(color='blue'))
fig.add_scatter(x=test['ds'], y=test['y'], mode='lines', name='test', line=dict(color='green'))
fig.add_scatter(x=xgboost_predictions['ds'], y=xgboost_predictions['XGBRegressor'], mode='lines', name='prediction', line=dict(color='red'))

fig.update_layout(template='plotly_white', width=1000, height=500)
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="closing price")
fig.show()


xgboost_mape = my_mape(xgboost_predictions['XGBRegressor'], test['y'])


model_metrics_logger.info(
    f'Model name: XGBoost, MAPE score: {xgboost_mape}, random_state: {random_state}, hyperparameters: {xgboost_grid_search.get_best_params()}'
)

Model name: XGBoost, MAPE score: 6.767184200955984, random_state: 42, hyperparameters: {'lags': [1, 2, 3, 4, 5], 'max_depth': 6, 'reg_lambda': 0.01, 'eta': 0.4}


def ltsm_model_init(params):    
    return NeuralForecast(
        freq='D',
        models=[
            LSTM(
                h=len(test), # Forecast horizon
                max_steps=100, # Number of steps to train
                scaler_type='standard', # Type of scaler to normalize data
                encoder_hidden_size=params['encoder_hidden_size'],#64, # Defines the size of the hidden state of the LSTM
                decoder_hidden_size=params['decoder_hidden_size'],#64,
                context_size=params['context_size'],#10,
                encoder_dropout=params['encoder_dropout'],#0,
                learning_rate=0.001,
                random_seed=random_state
            )
        ]
    )


def ltsm_cv_score(model): 
    cv_results = model.cross_validation(
        df = train,
        step_size = 40,
        n_windows = 5,
        refit=True
    )
        
    cv_results = evaluate_cross_validation(cv_results, [mape])

    return cv_results.loc['stock_id', 'mape']['LSTM']


ltsm_grid_search = GridSearch(
    all_params={
        'encoder_hidden_size': [32, 64, 128],
        'decoder_hidden_size': [32, 64, 128],
        'context_size': [5, 10, 20],
        'encoder_dropout': [0, 0.2, 0.4],
    },
    model_initializer=ltsm_model_init,
    calculate_score=ltsm_cv_score
)


ltsm_grid_search.search()

Seed set to 42

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Training: |                                                                                      | 0/? [00:00<…

Validation: |                                                                                    | 0/? [00:00<…

Predicting: |                                                                                    | 0/? [00:00<…

Sanity Checking: |                                                                               | 0/? [00:00<…

Training: |                                                                                      | 0/? [00:00<…

Validation: |                                                                                    | 0/? [00:00<…

Predicting: |                                                                                    | 0/? [00:00<…

Sanity Checking: |                                                                               | 0/? [00:00<…


ltsm_grid_search.display_results()


ltsm_model = ltsm_model_init(ltsm_grid_search.get_best_params())
ltsm_model.fit(train)

Seed set to 42

Sanity Checking: |                                                                               | 0/? [00:00<…

Training: |                                                                                      | 0/? [00:00<…

Validation: |                                                                                    | 0/? [00:00<…


ltsm_predictions = ltsm_model.predict()

ltsm_predictions.set_index(test.index, inplace=True)

Predicting: |                                                                                    | 0/? [00:00<…


fig = px.line(title="Nvidia closing prices (daily)")
fig.add_scatter(x=train['ds'], y=train['y'], mode='lines', name='train', line=dict(color='blue'))
fig.add_scatter(x=test['ds'], y=test['y'], mode='lines', name='test', line=dict(color='green'))
fig.add_scatter(x=ltsm_predictions['ds'], y=ltsm_predictions['LSTM'], mode='lines', name='prediction', line=dict(color='red'))

fig.update_layout(template='plotly_white', width=1000, height=500)
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="closing price")
fig.show()


ltsm_mape = my_mape(ltsm_predictions['LSTM'], test['y'])


model_metrics_logger.info(
    f'Model name: LSTM, MAPE score: {ltsm_mape}, random_state: {random_state}, hyperparameters: {ltsm_grid_search.get_best_params()}'
)

Model name: LSTM, MAPE score: 7.760950895078518, random_state: 42, hyperparameters: {'encoder_hidden_size': 128, 'decoder_hidden_size': 128, 'context_size': 10, 'encoder_dropout': 0}


def arima_model_init(params):    
    return StatsForecast(
        freq='D',
        models=[
            AutoARIMA(),
        ]
    )


arima_model = arima_model_init(None)
arima_model.fit(train)

StatsForecast(models=[AutoARIMA])


#get result to analyze residuals later
arima_result=arima_model.fitted_[0,0].model_
arima_result

{'coef': {'ar1': -0.2950615409923522,
  'ar2': -0.7514466300195752,
  'ma1': 0.21164529710605853,
  'ma2': 0.8111743661957798,
  'ma3': -0.09865802999200439,
  'drift': 0.18062057096145087},
 'sigma2': 3.341184844962976,
 'var_coef': array([[4.97817197e-03, 5.48444802e-05, 0.00000000e+00, 0.00000000e+00,
         0.00000000e+00, 0.00000000e+00],
        [5.48444802e-05, 3.25549646e-04, 0.00000000e+00, 0.00000000e+00,
         0.00000000e+00, 0.00000000e+00],
        [0.00000000e+00, 0.00000000e+00, 1.71526587e-03, 0.00000000e+00,
         0.00000000e+00, 0.00000000e+00],
        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.71526587e-03,
         0.00000000e+00, 0.00000000e+00],
        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
         1.71526587e-03, 0.00000000e+00],
        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
         0.00000000e+00, 1.71526587e-03]]),
 'mask': array([ True,  True,  True,  True,  True,  True]),
 'loglik': -1175.9484200614384,
 'aic': 2365.8968401228767,
 'arma': (2, 3, 0, 0, 1, 1, 0),
 'residuals': array([ 1.90113696e-02, -1.06078399e-01, -1.22416591e-01,  2.81102130e-01,
         4.00879243e-01, -5.70754063e-01, -6.37218095e-01, -5.47684314e-01,
         1.63864788e-01,  1.19152166e+00,  6.49721687e-01, -7.78642063e-01,
        -2.23525236e-01, -8.88221279e-02, -2.50977963e-01,  8.12067089e-01,
         2.76814950e-03, -1.00354548e-01, -1.28525852e+00, -5.77960545e-02,
         9.57142797e-02, -1.04053711e-01,  9.32717014e-01, -2.09539749e-01,
        -1.02647633e+00, -8.88933708e-01, -3.02103726e-01, -3.84813536e-01,
        -5.08387440e-01, -3.57290156e-01,  3.52625984e-02,  2.63033922e+00,
        -4.06124723e-01, -3.00041475e-01,  9.64528086e-02, -3.93979142e-02,
        -6.78763263e-01, -7.18482876e-01,  5.67217568e-01,  3.99741680e-01,
        -4.70937413e-01, -2.44751645e-01, -1.15373983e-01, -5.89120625e-01,
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         1.13100327e+00,  4.72297755e-01, -4.76524336e-01, -3.99494880e-02,
         5.56294206e-01,  1.27868812e+00,  1.49670897e+00, -2.64890598e-01,
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         3.41391195e-02, -6.92183387e-01, -3.09346925e-01, -1.75606837e-01,
         9.48542101e-01,  1.07891476e+00,  1.11949706e+00, -1.39560730e+00,
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         21.09666667,  21.09433333,  21.092     ,  22.174     ,
         22.206     ,  22.336     ,  21.258     ,  21.43666667,
         21.61533333,  21.794     ,  22.971     ,  22.753     ,
         21.998     ,  21.386     ,  21.2015    ,  21.017     ,
         20.8325    ,  20.648     ,  20.796     ,  23.659     ,
         23.287     ,  23.359     ,  23.431     ,  23.503     ,
         23.218     ,  22.714     ,  23.315     ,  23.89      ,
         23.783     ,  23.676     ,  23.569     ,  23.285     ,
         24.184     ,  23.455     ,  22.962     ,  22.96066667,
         22.95933333,  22.958     ,  24.043     ,  24.235     ,
         25.541     ,  25.731     ,  25.78766667,  25.84433333,
         25.901     ,  26.204     ,  26.451     ,  27.195     ,
         26.776     ,  26.7       ,  26.624     ,  26.548     ,
         26.415     ,  26.991     ,  27.387     ,  27.791     ,
         27.85333333,  27.91566667,  27.978     ,  27.472     ,
         26.879     ,  27.034     ,  27.173     ,  27.312     ,
         27.451     ,  27.59      ,  27.169     ,  26.502     ,
         26.456     ,  26.759     ,  26.83966667,  26.92033333,
         27.001     ,  27.674     ,  27.933     ,  27.098     ,
         27.127     ,  27.09866667,  27.07033333,  27.042     ,
         26.234     ,  26.957     ,  27.221     ,  27.747     ,
         28.13766667,  28.52833333,  28.919     ,  28.204     ,
         27.802     ,  27.558     ,  28.679     ,  28.838     ,
         28.997     ,  29.156     ,  28.566     ,  28.879     ,
         28.576     ,  28.336     ,  28.54366667,  28.75133333,
         28.959     ,  29.212     ,  30.185     ,  31.673     ,
         31.262     ,  31.233     ,  31.204     ,  31.175     ,
         30.69      ,  30.527     ,  37.984     ,  38.937     ,
         39.23175   ,  39.5265    ,  39.82125   ,  40.116     ,
         37.857     ,  39.768     ,  39.322     ,  39.27633333,
         39.23066667,  39.185     ,  38.666     ,  37.485     ,
         38.523     ,  38.781     ,  39.01766667,  39.25433333,
         39.491     ,  41.024     ,  42.986     ,  42.659     ,
         42.697     ,  42.975     ,  43.253     ,  43.531     ,
         43.809     ,  43.049     ,  43.016     ,  42.212     ,
         41.68266667,  41.15333333,  40.624     ,  41.88      ,
         41.126     ,  40.845     ,  42.291     ,  42.32966667,
         42.36833333,  42.407     ,  42.365     ,  42.323     ,
         42.1       ,  42.498     ,  42.393     ,  42.288     ,
         42.183     ,  42.398     ,  43.89      ,  45.976     ,
         45.474     ,  45.80266667,  46.13133333,  46.46      ,
         47.492     ,  47.066     ,  45.54      ,  44.327     ,
         44.42133333,  44.51566667,  44.61      ,  45.66      ,
         45.449     ,  45.9       ,  46.739     ,  46.73666667,
         46.73433333,  46.732     ,  46.506     ,  44.266     ,
         44.517     ,  44.686     ,  44.92866667,  45.17133333,
         45.414     ,  44.657     ,  42.551     ,  42.372     ,
         40.875     ,  41.83866667,  42.80233333,  43.766     ,
         43.933     ,  43.492     ,  43.35      ,  43.287     ,
         44.51066667,  45.73433333,  46.958     ,  45.67      ,
         47.123     ,  47.174     ,  46.003     ,  46.27733333,
         46.55166667,  46.826     ,  48.774     ,  49.266     ,
         49.363     ,  48.514     ,  48.52075   ,  48.5275    ,
         48.53425   ,  48.541     ,  47.055     ,  46.239     ,
         45.563     ,  45.43533333,  45.30766667,  45.18      ,
         44.861     ,  45.498     ,  45.593     ,  43.899     ,
         43.92133333,  43.94366667,  43.966     ,  43.517     ,
         42.225     ,  41.005     ,  41.632     ,  41.831     ,
         42.03      ,  42.229     ,  41.898     ,  42.47      ,
         43.071     ,  43.49      ,  43.92333333,  44.35666667,
         44.79      ,  43.539     ,  44.012     ,  44.677     ,
         45.763     ,  45.59766667,  45.43233333,  45.267     ,
         45.798     ,  46.815     ,  46.935     ,  45.46      ,
         45.667     ,  45.874     ,  46.081     ,  43.954     ,
         42.197     ,  42.096     ,  41.375     ,  41.91266667,
         42.45033333,  42.988     ,  43.671     ,  41.782     ,
         40.322     ,  40.495     ,  40.71633333,  40.93766667,
         41.159     ,  40.782     ,  42.316     ,  43.512     ,
         45.011     ,  45.26166667,  45.51233333,  45.763     ,
         45.967     ,  46.566     ,  46.955     ,  48.335     ,
         48.426     ,  48.517     ,  48.608     ,  49.662     ,
         48.899     ,  49.486     ,  49.3       ,  49.67466667,
         50.04933333,  50.424     ,  49.982     ,  48.703     ,
         48.2315    ,  47.76      ,  47.91866667,  48.07733333,
         48.236     ,  47.821     ,  48.141     ,  46.812     ,
         46.758     ,  46.33833333,  45.91866667,  45.499     ,
         46.579     ,  45.534     ,  46.603     ,  47.508     ,
         47.21833333,  46.92866667,  46.639     ,  47.666     ,
         48.092     ,  48.342     ,  48.913     ,  49.303     ,
         49.693     ,  50.083     ,  49.614     ,  48.135     ,
         48.992     ,  48.823     ,  48.936     ,  49.049     ,
         49.162     ,  49.275     ,  49.423     ,  49.535     ,
         49.526     ,  49.1915    ,  48.857     ,  48.5225    ,
         48.188     ,  47.566     ,  47.983     ,  49.092     ,
         50.14      ,  51.188     ,  52.236     ,  53.14      ,
         54.343     ,  54.818     ,  54.704     ,  55.12825   ,
         55.5525    ,  55.97675   ,  56.401     ,  56.051     ,
         57.102     ,  59.499     ,  59.55433333,  59.60966667,
         59.665     ,  59.867     ,  61.365     ,  61.656     ,
         61.024     ,  61.50566667,  61.98733333,  62.469     ,
         62.786     ,  61.5       ,  63.035     ,  66.153     ,
         67.202     ,  68.251     ,  69.3       ,  68.231     ,
         70.082     ,  69.66      ,  72.108     ,  72.15566667,
         72.20333333,  72.251     ,  72.182     ,  73.897     ,
         72.683     ,  72.589     ,  71.8135    ,  71.038     ,
         70.2625    ,  69.487     ,  67.49      ,  78.527     ,
         78.847     ,  78.934     ,  79.021     ,  79.108     ,
         78.761     ,  77.69      ,  79.018     ,  82.274     ,
         83.26166667,  84.24933333,  85.237     ,  85.982     ,
         88.734     ,  92.588     ,  87.502     ,  86.93833333,
         86.37466667,  85.811     ,  91.937     ,  90.926     ,
         87.962     ,  87.841     ,  88.04833333,  88.25566667,
         88.463     ,  89.397     ,  90.371     ,  91.435     ,
         94.25      ,  94.50033333,  94.75066667,  95.001     ,
         92.521     ,  90.269     ,  90.25      ,  90.28125   ,
         90.3125    ,  90.34375   ,  90.375     ,  89.5       ,
         88.966     ,  85.89      ,  88.024     ,  87.73      ,
         87.436     ,  87.142     ,  85.335     ,  87.039     ,
         90.617     ,  88.23      ,  87.47633333,  86.72266667,
         85.969     ,  87.4       ,  84.039     ,  84.675     ,
         76.221     ,  77.31166667,  78.40233333,  79.493     ,
         82.38      ,  79.651     ,  82.686     ,  87.798     ,
         87.81066667,  87.82333333,  87.836     ,  86.377     ,
         83.06      ,  85.8375    ,  88.78      ,  89.89366667,
         91.00733333,  92.121     ,  90.542     ,  90.396     ,
         88.79      ,  89.859     ,  90.04866667,  90.23833333,
         90.428     ,  91.357     ,  94.649     ,  94.4       ,
         92.483     ,  93.25866667,  94.03433333,  94.81      ,
         95.377     ,  94.898     , 103.775     , 106.411     ,
        108.28075   , 110.1505    , 112.02025   , 113.89      ,
        114.681     , 110.526     , 109.502     , 111.33466667,
        113.16733333, 115.        , 116.443     , 122.449     ,
        120.941     , 120.865     , 121.13      , 121.395     ,
        121.66      , 120.91      , 125.26      , 129.32      ,
        131.93      , 131.61666667, 131.30333333, 130.99      ,
        135.66      , 133.29      , 130.92      , 127.        ,
        124.02333333, 121.04666667, 118.07      , 126.18      ,
        126.44      , 123.95      , 123.52      , 123.74666667,
        123.97333333, 124.2       , 122.69      , 128.27      ,
        127.04      , 125.81      , 126.58666667, 127.36333333,
        128.14      , 131.43      , 134.84      , 127.48      ,
        129.13      , 128.89666667, 128.66333333, 128.43      ,
        126.32      , 117.91      , 121.11      , 118.        ,
        119.88      , 121.76      , 123.64      , 122.43      ,
        114.39      , 112.4       , 113.07      , 112.55666667,
        112.04333333, 111.53      , 103.69      , 117.13      ,
        109.38      , 107.31      , 105.08      , 102.85      ,
        100.62      , 104.        ,  98.75      , 105.16      ,
        104.7       , 106.16666667, 107.63333333, 109.1       ,
        116.15      , 117.96      , 122.91      , 124.57      ,
        126.37666667, 128.18333333, 129.99      , 127.27      ,
        128.42      , 123.86      , 129.26      , 128.34      ,
        127.42      , 126.5       , 128.32      , 125.57      ]),
 'lambda': None}


arima_name = arima_string(arima_result)
arima_name

'ARIMA(2,1,3) with drift        '


fitted_arima_predictions = pd.DataFrame(arima_result.get("residuals"), columns=["residuals"])
plot_residuals(fitted_arima_predictions['residuals'])


arima_predictions = arima_model.predict(len(test))

arima_predictions.set_index(test.index, inplace=True)


fig = px.line(title="Nvidia closing prices (daily)")
fig.add_scatter(x=train['ds'], y=train['y'], mode='lines', name='train', line=dict(color='blue'))
fig.add_scatter(x=test['ds'], y=test['y'], mode='lines', name='test', line=dict(color='green'))
fig.add_scatter(x=arima_predictions['ds'], y=arima_predictions['AutoARIMA'], mode='lines', name='prediction', line=dict(color='red'))

fig.update_layout(template='plotly_white', width=1000, height=500)
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="closing price")
fig.show()


arima_mape = my_mape(arima_predictions['AutoARIMA'], test['y'])


model_metrics_logger.info(
    f'Model name: {arima_name}, MAPE score: {arima_mape}'
)

Model name: ARIMA(2,1,3) with drift        , MAPE score: 6.181598482885323


def prophet_model_init(params):
    return fp.Prophet(**params, yearly_seasonality = False, weekly_seasonality = False) 
    #m.add_regressor('U', mode=best_params['seasonality_mode'])


def prophet_cv_score(model): 
    
    model.fit(train)    
    cv_results = cross_validation(model, initial='384 days', period='40 days', horizon = '40 days', parallel="processes")
    df_p = performance_metrics(cv_results, rolling_window=1)
    return df_p.loc[0, 'mape']


prophet_grid_search = GridSearch(
    all_params={
        'changepoint_prior_scale': [0.005, 0.05, 0.5],
        'n_changepoints': [10, 25, 100],
        'changepoint_range': [0.7 ,0.8, 0.9]
    },
    model_initializer=prophet_model_init,
    calculate_score=prophet_cv_score
)


prophet_grid_search.search()

13:05:10 - cmdstanpy - INFO - Chain [1] start processing
13:05:10 - cmdstanpy - INFO - Chain [1] done processing
13:05:20 - cmdstanpy - INFO - Chain [1] start processing

Iteration: 1 of 27
	Params: {'changepoint_prior_scale': 0.005, 'n_changepoints': 10, 'changepoint_range': 0.7}
	Score: 0.14512832582445834

13:05:20 - cmdstanpy - INFO - Chain [1] done processing
13:05:22 - cmdstanpy - INFO - Chain [1] start processing

Iteration: 2 of 27
	Params: {'changepoint_prior_scale': 0.005, 'n_changepoints': 10, 'changepoint_range': 0.8}
	Score: 0.14864800814797133

13:05:22 - cmdstanpy - INFO - Chain [1] done processing
13:05:25 - cmdstanpy - INFO - Chain [1] start processing

Iteration: 3 of 27
	Params: {'changepoint_prior_scale': 0.005, 'n_changepoints': 10, 'changepoint_range': 0.9}
	Score: 0.14906051058841607

13:05:25 - cmdstanpy - INFO - Chain [1] done processing

Iteration: 4 of 27
	Params: {'changepoint_prior_scale': 0.005, 'n_changepoints': 25, 'changepoint_range': 0.7}
	Score: 0.1408405729564439

13:05:27 - cmdstanpy - INFO - Chain [1] start processing
13:05:27 - cmdstanpy - INFO - Chain [1] done processing
13:05:30 - cmdstanpy - INFO - Chain [1] start processing

Iteration: 5 of 27
	Params: {'changepoint_prior_scale': 0.005, 'n_changepoints': 25, 'changepoint_range': 0.8}
	Score: 0.1450305760772098


prophet_grid_search.display_results()


prophet_model = prophet_model_init(prophet_grid_search.get_best_params())

prophet_model.fit(train) 

future = prophet_model.make_future_dataframe(periods=len(test))
#future['U'] = regressors_per_city['U'] # Future regressor U                     
forecast_prophet = prophet_model.predict(future)

13:06:50 - cmdstanpy - INFO - Chain [1] start processing
13:06:51 - cmdstanpy - INFO - Chain [1] done processing


fitted_forecast_prophet = pd.DataFrame(train['y'] - forecast_prophet[:len(train)]['yhat'], columns=["residuals"])
plot_residuals(fitted_forecast_prophet['residuals'])


fig = px.line(title="Nvidia closing prices (daily)")
fig.add_scatter(x=train['ds'], y=train['y'], mode='lines', name='train', line=dict(color='blue'))
fig.add_scatter(x=test['ds'], y=test['y'], mode='lines', name='test', line=dict(color='green'))
fig.add_scatter(x=forecast_prophet['ds'], y=forecast_prophet['yhat'], mode='lines', name='prediction', line=dict(color='red'))

fig.update_layout(template='plotly_white', width=1000, height=500)
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="closing price")
fig.show()


prophet_mape = my_mape(forecast_prophet['yhat'], test['y'])


model_metrics_logger.info(
    f'Model name: Prophet, MAPE score: {prophet_mape}, hyperparameters: {prophet_grid_search.get_best_params()}'
)

Model name: Prophet, MAPE score: 13.272497223406763, hyperparameters: {'changepoint_prior_scale': 0.05, 'n_changepoints': 100, 'changepoint_range': 0.7}

	<TICKER>	<PER>	<DATE>	<TIME>	<OPEN>	<HIGH>	<LOW>	<CLOSE>	<VOL>
0	NVDA	D	23/01/23	00:00:00	18.0690	19.245	17.819	19.192	15035650
1	NVDA	D	24/01/23	00:00:00	18.8370	19.484	18.825	19.265	9503480
2	NVDA	D	25/01/23	00:00:00	18.9125	19.365	18.581	19.332	8723800
3	NVDA	D	26/01/23	00:00:00	19.7240	20.162	19.278	19.799	8941270
4	NVDA	D	27/01/23	00:00:00	19.4620	20.627	19.424	20.356	10233070

	<TICKER>	<PER>	<DATE>	<TIME>	<OPEN>	<HIGH>	<LOW>	<CLOSE>	<VOL>
ds
2023-01-23	NVDA	D	23/01/23	00:00:00	18.0690	19.245	17.819	19.192	15035650
2023-01-24	NVDA	D	24/01/23	00:00:00	18.8370	19.484	18.825	19.265	9503480
2023-01-25	NVDA	D	25/01/23	00:00:00	18.9125	19.365	18.581	19.332	8723800
2023-01-26	NVDA	D	26/01/23	00:00:00	19.7240	20.162	19.278	19.799	8941270
2023-01-27	NVDA	D	27/01/23	00:00:00	19.4620	20.627	19.424	20.356	10233070

	ds	y	unique_id
0	2023-01-23	19.1920	stock_id
1	2023-01-24	19.2650	stock_id
2	2023-01-25	19.3320	stock_id
3	2023-01-26	19.7990	stock_id
4	2023-01-27	20.3560	stock_id
...	...	...	...
725	2025-01-17	137.7400	stock_id
726	2025-01-18	138.5125	stock_id
727	2025-01-19	139.2850	stock_id
728	2025-01-20	140.0575	stock_id
729	2025-01-21	140.8300	stock_id

Data preporation¶

Split dataset on train and test¶

Stationarity analisys and differencing¶

Autocorelation analysys¶

Lineral Regression¶

XGBoost¶

LTSM¶

ARIMA¶

Prophet¶

Summary¶

	score	lags	polynomial	standard_scaller	ridge_alpha
10	0.208503	[1]	2	True	0.01
15	0.208504	[1]	2	False	0.01
11	0.208505	[1]	2	True	0.10
16	0.208507	[1]	2	False	0.10
12	0.208525	[1]	2	True	1.00
30	0.208537	[1, 2]	2	True	0.01
35	0.208537	[1, 2]	2	False	0.01
31	0.208539	[1, 2]	2	True	0.10
36	0.208540	[1, 2]	2	False	0.10
17	0.208541	[1]	2	False	1.00
32	0.208556	[1, 2]	2	True	1.00
37	0.208572	[1, 2]	2	False	1.00
13	0.208714	[1]	2	True	10.00
33	0.208726	[1, 2]	2	True	10.00
29	0.208738	[1, 2]	1	False	100.00
24	0.208765	[1, 2]	1	True	100.00
28	0.208836	[1, 2]	1	False	10.00
23	0.208841	[1, 2]	1	True	10.00
27	0.208851	[1, 2]	1	False	1.00
22	0.208852	[1, 2]	1	True	1.00
26	0.208853	[1, 2]	1	False	0.10
21	0.208853	[1, 2]	1	True	0.10
25	0.208853	[1, 2]	1	False	0.01
20	0.208853	[1, 2]	1	True	0.01
18	0.208855	[1]	2	False	10.00
38	0.208868	[1, 2]	2	False	10.00
9	0.208918	[1]	1	False	100.00
4	0.208965	[1]	1	True	100.00
8	0.209083	[1]	1	False	10.00
3	0.209092	[1]	1	True	10.00
7	0.209108	[1]	1	False	1.00
2	0.209109	[1]	1	True	1.00
6	0.209111	[1]	1	False	0.10
1	0.209111	[1]	1	True	0.10
5	0.209111	[1]	1	False	0.01
0	0.209111	[1]	1	True	0.01
49	0.209330	[1, 2, 3]	1	False	100.00
44	0.209359	[1, 2, 3]	1	True	100.00
48	0.209445	[1, 2, 3]	1	False	10.00
43	0.209451	[1, 2, 3]	1	True	10.00
47	0.209463	[1, 2, 3]	1	False	1.00
42	0.209464	[1, 2, 3]	1	True	1.00
46	0.209465	[1, 2, 3]	1	False	0.10
41	0.209465	[1, 2, 3]	1	True	0.10
45	0.209465	[1, 2, 3]	1	False	0.01
40	0.209465	[1, 2, 3]	1	True	0.01
34	0.209941	[1, 2]	2	True	100.00
14	0.210032	[1]	2	True	100.00
39	0.210645	[1, 2]	2	False	100.00
19	0.210701	[1]	2	False	100.00
50	0.212407	[1, 2, 3]	2	True	0.01
55	0.212408	[1, 2, 3]	2	False	0.01
51	0.212409	[1, 2, 3]	2	True	0.10
56	0.212411	[1, 2, 3]	2	False	0.10
52	0.212423	[1, 2, 3]	2	True	1.00
57	0.212442	[1, 2, 3]	2	False	1.00
53	0.212556	[1, 2, 3]	2	True	10.00
58	0.212719	[1, 2, 3]	2	False	10.00
54	0.213438	[1, 2, 3]	2	True	100.00
59	0.214049	[1, 2, 3]	2	False	100.00

	score	lags	max_depth	reg_lambda	eta
32	0.169367	[1, 2, 3, 4, 5]	6	0.01	0.4
24	0.173007	[1, 2, 3, 4, 5]	4	10.00	0.2
2	0.177048	[1, 2, 3, 4, 5]	2	0.01	0.4
25	0.178783	[1, 2, 3, 4, 5]	4	10.00	0.3
37	0.185258	[1, 2, 3, 4, 5]	6	1.00	0.3
41	0.186492	[1, 2, 3, 4, 5]	6	10.00	0.4
28	0.187684	[1, 2, 3, 4, 5]	4	100.00	0.3
43	0.188595	[1, 2, 3, 4, 5]	6	100.00	0.3
44	0.189341	[1, 2, 3, 4, 5]	6	100.00	0.4
40	0.189681	[1, 2, 3, 4, 5]	6	10.00	0.3
14	0.190060	[1, 2, 3, 4, 5]	2	100.00	0.4
13	0.190292	[1, 2, 3, 4, 5]	2	100.00	0.3
10	0.191773	[1, 2, 3, 4, 5]	2	10.00	0.3
35	0.192021	[1, 2, 3, 4, 5]	6	0.10	0.4
30	0.192454	[1, 2, 3, 4, 5]	6	0.01	0.2
27	0.192785	[1, 2, 3, 4, 5]	4	100.00	0.2
26	0.193767	[1, 2, 3, 4, 5]	4	10.00	0.4
31	0.195043	[1, 2, 3, 4, 5]	6	0.01	0.3
9	0.196731	[1, 2, 3, 4, 5]	2	10.00	0.2
12	0.197483	[1, 2, 3, 4, 5]	2	100.00	0.2
34	0.198781	[1, 2, 3, 4, 5]	6	0.10	0.3
5	0.199250	[1, 2, 3, 4, 5]	2	0.10	0.4
38	0.199326	[1, 2, 3, 4, 5]	6	1.00	0.4
39	0.199645	[1, 2, 3, 4, 5]	6	10.00	0.2
29	0.201222	[1, 2, 3, 4, 5]	4	100.00	0.4
42	0.201419	[1, 2, 3, 4, 5]	6	100.00	0.2
8	0.202223	[1, 2, 3, 4, 5]	2	1.00	0.4
11	0.202837	[1, 2, 3, 4, 5]	2	10.00	0.4
36	0.205195	[1, 2, 3, 4, 5]	6	1.00	0.2
23	0.206342	[1, 2, 3, 4, 5]	4	1.00	0.4
22	0.207973	[1, 2, 3, 4, 5]	4	1.00	0.3
18	0.208509	[1, 2, 3, 4, 5]	4	0.10	0.2
1	0.209138	[1, 2, 3, 4, 5]	2	0.01	0.3
6	0.213177	[1, 2, 3, 4, 5]	2	1.00	0.2
20	0.214428	[1, 2, 3, 4, 5]	4	0.10	0.4
0	0.214961	[1, 2, 3, 4, 5]	2	0.01	0.2
16	0.215977	[1, 2, 3, 4, 5]	4	0.01	0.3
19	0.216990	[1, 2, 3, 4, 5]	4	0.10	0.3
3	0.218077	[1, 2, 3, 4, 5]	2	0.10	0.2
33	0.218704	[1, 2, 3, 4, 5]	6	0.10	0.2
4	0.222191	[1, 2, 3, 4, 5]	2	0.10	0.3
21	0.223598	[1, 2, 3, 4, 5]	4	1.00	0.2
7	0.231318	[1, 2, 3, 4, 5]	2	1.00	0.3
15	0.236678	[1, 2, 3, 4, 5]	4	0.01	0.2
17	0.239410	[1, 2, 3, 4, 5]	4	0.01	0.4

	score	encoder_hidden_size	decoder_hidden_size	context_size	encoder_dropout
75	0.228417	128	128	10	0.0
42	0.229185	64	64	20	0.0
43	0.231044	64	64	20	0.2
44	0.234139	64	64	20	0.4
78	0.235707	128	128	20	0.0
...	...	...	...	...	...
53	0.313528	64	128	20	0.4
19	0.314533	32	128	5	0.2
26	0.314582	32	128	20	0.4
52	0.327497	64	128	20	0.2
24	0.367103	32	128	20	0.0

	score	changepoint_prior_scale	n_changepoints	changepoint_range
15	0.107952	0.050	100	0.7
12	0.108788	0.050	25	0.7
9	0.108806	0.050	10	0.7
18	0.108985	0.500	10	0.7
24	0.109227	0.500	100	0.7
21	0.109240	0.500	25	0.7
16	0.137025	0.050	100	0.8
10	0.138478	0.050	10	0.8
13	0.139373	0.050	25	0.8
5	0.140247	0.005	25	0.9
3	0.140841	0.005	25	0.7
4	0.145031	0.005	25	0.8
0	0.145128	0.005	10	0.7
8	0.148594	0.005	100	0.9
7	0.148635	0.005	100	0.8
1	0.148648	0.005	10	0.8
6	0.148917	0.005	100	0.7
2	0.149061	0.005	10	0.9
19	0.153119	0.500	10	0.8
22	0.158203	0.500	25	0.8
25	0.159548	0.500	100	0.8
11	0.161390	0.050	10	0.9
17	0.171802	0.050	100	0.9
26	0.172266	0.500	100	0.9
23	0.176268	0.500	25	0.9
14	0.176399	0.050	25	0.9
20	0.183862	0.500	10	0.9